The Kelp Cultivation Potential in Coastal and Offshore Regions of Norway

Home , Ole Jacob Broch, Regions of Norway

ORIGINAL RESEARCH published: 18 January 2019 doi: 10.3389/fmars.2018.00529

Ole Jacob Broch 1*, Morten Omholt Alver 1, Trine Bekkby 2, Hege Gundersen 2, Silje Forbord 1, Aleksander Handå 1, Jorunn Skjermo 1 and Kasper Hancke 2

1 SINTEF Ocean, Trondheim, Norway, 2 Norwegian Institute for Water Research (NIVA), Oslo, Norway

We have evaluated the cultivation potential of sugar kelp (Saccharina latissima) as a function of latitude and position (near- and offshore) along the Norwegian coast using a coupled 3D hydrodynamic-biogeochemical-kelp model system (SINMOD) run for four growth seasons (2012–2016). The results are spatially explicit and may be used to compare the suitability of different regions for kelp cultivation, both inshore and offshore.The simulation results were compared with growth data from kelp cultivation experiments and in situ observations on coverage of naturally growing kelp. The model Edited by: demonstrated a higher production potential offshore than in inshore regions, which is António V. Sykes, Centro de Ciências do Mar (CCMAR), mainly due to the limitations in nutrient availability caused by the stratification found along Portugal the coast. However, suitable locations for kelp cultivation were also identified in areas with Reviewed by: high vertical mixing close to the shore. The results indicate a latitudinal effect on the timing Mark Johnson, National University of Ireland Galway, of the optimal period of growth, with the prime growth period being up to 2 months earlier Ireland in the south (58 ◦N) than in the north (71 ◦N). Although the maximum cultivation potential Oscar Sosa-Nishizaki, was similar in the six marine ecoregions in Norway (150–200 tons per hectare per year), Ensenada Center for Scientific Research and Higher Education the deployment time of the cultures seems to matter significantly in the south, but less (CICESE), Mexico so in the north. The results are discussed, focusing on their potential significance for *Correspondence: optimized cultivation and to support decision making toward sustainable management. Ole Jacob Broch [email protected] Keywords: Saccharina latissima, macroalgae cultivation, mathematical model, offshore aquaculture, ecosystem model, marine biomass, environmental variables, latitude Specialty section: This article was submitted to Marine Fisheries, Aquaculture and INTRODUCTION Living Resources, a section of the journal In order to meet the future challenges of limited terrestrial food resources, a greater part of the Frontiers in Marine Science human food consumption will need to be based on marine production, at lower trophic levels than Received: 09 October 2018 today (Olsen, 2011). The demand for marine biomass for other purposes, such as biofuels, feed Accepted: 31 December 2018 for animals, cosmetics, medicines, pharmaceuticals and raw materials for the biotechnological Published: 18 January 2019 industry, is also expected to increase (Holdt and Kraan, 2011; Kraan, 2016). Citation: Macroalgae are, by volume, the largest group of species in aquaculture, with a global Broch OJ, Alver MO, Bekkby T, production of 2.94 × 107 tons wet weight per year (FAO, 2018). More than 99.9% of Gundersen H, Forbord S, Handå A, this is produced in Asia (Ibid.), but the interest in macroalgal aquaculture in the Western Skjermo J and Hancke K (2019) The Kelp Cultivation Potential in Coastal hemisphere is increasing. It has been suggested that by 2050, the value of the kelp industry 9 and Offshore Regions of Norway. may have a turnover of 4 × 10 Euro per year in Norway alone, with a production of 7 Front. Mar. Sci. 5:529. 2 × 10 t per year (Olafsen et al., 2012). Globally, the potential for marine macroalgal doi: 10.3389/fmars.2018.00529 cultivation has been suggested to lie at 109 to 1011 t dry weight (DW) (Lehahn et al., 2016).

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There are many sectors and interests competing for space results from both cultivation experiments with S. latissima and in coastal regions (Hersoug, 2013), including cultivation of surveys of the density of natural kelp populations in order to macroalgae (Duarte et al., 2017). In order to manage the provide an evaluation of the realism of the results. coastal zone, tools that can assess the suitability of different locations for different purposes are necessary. In order to grant MATERIALS AND METHODS aquaculture permits for macroalgae production, knowledge of the production potential and spatially explicit maps of suitable Coupled Hydrodynamic-Ecosystem-Kelp areas for production are needed. These maps must be based on Model System (SINMOD) physical, biological and biogeochemical variables important for Model Description the survival and growth of macroalgae. While most naturally SINMOD is a coupled 3 dimensional hydrodynamic- growing macroalgae depend on some form of solid substrate biogeochemical model system (Slagstad and McClimans, 2005). and therefore grow mostly on the bottom as deep as the The biogeochemical component includes compartments for, e.g., − + light penetration allows, macroalgal cultivation facilities may in concentrations of nitrate (NO3 ), ammonium (NH4 ), silicate, principle be sited regardless of the seabed depth. This allows detritus, bacteria, phytoplankton, ciliates and zooplankton for great possibilities in the choice of cultivation sites and fewer (Wassmann et al., 2006). Phytoplankton production and nutrient direct conflicts with natural macroalgal communities. uptake are calculated as combined effects of temperature, light, Kelps are among the most important macroalgae in and nutrient concentrations. The phytoplankton is grazed aquaculture. The main environmental variables for the growth by ciliates and zooplankton, and dead/respired matter enter of kelps are nutrients, light, temperature, and ocean currents the detritus and ammonium compartments and are further (Wiencke and Bischof, 2012). For naturally growing kelps, wave remineralized into nitrate and silicate. The attenuation of light exposure is also important, since waves keep the fronds free from is calculated from the background attenuation of the sea water sedimentation and epiphytic growth, while maximizing the light and the concentration of phytoplankton. The details are given in trapping area and sustaining a good flux of nutrients. Wassmann et al. (2006). Biogeochemical conditions are determined by physical factors. An individual based growth model for sugar kelp has been The Norwegian Coastal Current (NCC) is one of the main developed and coupled with SINMOD (Broch and Slagstad, 2012; physical drivers of the Norwegian coastal ecosystem. The NCC Broch et al., 2013; Fossberg et al., 2018). The state variables is driven by the outflow from the Baltic Sea and transports water in the growth model are frond area (A, unit: cm2), internal northward along the Norwegian coast where it gets replenished nitrogen reserves (N, unit: g N (g structural mass)−1), and with freshwater and nutrients from rivers and fjords along the internal carbon reserves (C, unit: g C (g structural mass)−1). way (Sætre, 2007). This leads to periods of strong stratification, The environmental variables used to force kelp growth in the especially in fjords and in the NCC itself, and thus to nutrient model are temperature, salinity, light intensity (PAR), nutrient − + depletion in the surface layer in spring/early summer following (NO3 , NO4 ) concentrations, water current speed and latitude the pelagic spring bloom. This has implications for the nutrients (implicitly through the day length) (Broch and Slagstad, 2012; available for macroalgal primary production. Broch et al., 2013; Fossberg et al., 2018) (Figure 1). Salinities The main objective of the present paper was to investigate below 25 PSU are assumed to cause reduced growth, nutrient how the aquaculture production potential for the commercially uptake and photosynthetic rates (Bartsch et al., 2008; Mortensen, important kelp species Saccharina latissima varies along and 2017). This effect has been introduced in the model by outside the Norwegian coast. While there have been many multiplying those rates by the factor surveys on the production and biomass in natural kelp forests in Norway (Abdullah and Fredriksen, 2004; Moy et al., 2009; Gundersen et al., 2012), information on the cultivation potential 1, for S ≥ 25 S−25 for kelps outside natural kelp forests is largely missing on fsalinity =  1 + 18 , for 16 ≤ S < 25  S a coastal scale. In absence of large, high resolution data 32 , for 0 ≤ S < 16 sets on industrial cultivation yields, a coupled hydrodynamic-  where S denotes the salinity (PSU). biogeochemical ocean model system (SINMOD; Wassmann The effect of water current speed has been modified from that et al., 2006) was used. The model has been further coupled in Broch et al. (2013). In the present version, the uptake rate of with an individual based growth model for S. latissima (Broch nutrients, J, is calculated as et al., 2013; Fossberg et al., 2018). Although dynamical modeling can never replace field experiments and surveys, the simulations provide results resolved in time and space that are especially J = fcurrentJN useful in “data poor” contexts. Thus, both geographic and seasonal/interannual variations in the cultivation potential are where JN is the uptake rate per unit frond area based on external considered, in addition to how these interplay with hydrographic and internal nutrient reserves, and the water current speed, U and biogeochemical variables. In particular, nearshore to offshore (ms−1), is taken into account by the factor and latitudinal gradients in the production yields and timing are considered. The simulation results on S. latissima frond sizes, a −U/U0 proxy for the biomass production potential, are compared with fcurrent (U) = a 1 − e + b,

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FIGURE 1 | Schematic for the main processes in the kelp growth model and how the state variables are linked with the environmental variables simulated by the 3D ocean model.

−1 with a = 0.72, b = 0.28, and U0 = 0.045 ms (Hurd et al., 1996; Broch and Slagstad, 2012). The additional constant term b ensures a substantial nutrient uptake even for very low current speeds (Broch et al., 2013). The numerical values of the parameters have been chosen such that f current(0.03) = 0.63 (Hurd et al., 1996; Broch and Slagstad, 2012). The effect of day lengths and changes in day length is accounted for using Broch and Slagstad (2012). For latitudes L above the polar circle (> 66.5 ◦N) a modified version of the photoperiod effect f photo at Julian day number n is applied as follows:

fphoto (n, L) = 0.85 1 + δ(n, L) + 0.3, L > 66.5, where FIGURE 2 | The growth rate modiﬁer fphoto, taking into account the effect of changes in the day length, plotted as a function of the day in the year for three latitudes. 0, λ (n, L) = 0 δ (n, L) = . sgn(λ n, l , λ(n.L) = 0 Here, λ (n, L) denotes the normalized change in day length at day coas.oregonstate.edu/research/po/research/tide/global.html). number n and latitude L. See Figure 2. ERA-Interim data from ECMWF (Dee et al., 2011) were used for atmospheric forcing. Data on freshwater discharges from rivers Model Simulations and land were collected from simulations by the Norwegian Four SINMOD model domains with 800 m horizontal resolution, Water Resources and Energy Directorate (www.nve.no). The together covering the entire Norwegian coast, were used. The simulations were performed using a version of the HBV-model model was nested from a 20 km model setup (Wassmann in 1 km horizontal resolution (Beldring et al., 2003). et al., 2006) to a setup of 4 km horizontal resolution for the The simulation time steps used in the 800m simulations were Northeastern Atlantic, and ﬁnally to the 800 m resolution model 72 and 120 s, depending on horizontal resolution and bathymetry domains (Figure 3). Vertical layers of thickness ranging from via standard numerical stability criteria. 3m near the surface to 50–100m at greater depths were used. The simulations using the 800 m models were run for a period The bathymetry data for the model domains was provided by of 4 entire growth seasons, from autumn 2012 until summer the Norwegian Mapping Authority (www.kartverket.no) with 2016. The 20/4km models were subjected to a spin up period of additional data from IBCAO (www.ibcao.org). ∼20 years prior to the simulation start. The 20 km model was forced by tidal components M2, For each growth season, each grid cell down to 8 m depth S2, K1, and N2 at the open boundaries with data on global was initialized with the same kelp state variable values at the ocean tides imported from the TPXO 6.2 model (http://www. deployment dates. The initial values used were

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FIGURE 3 | The model domain for the Nordic Seas in 4 km horizontal resolution used to produce boundary conditions for the coastal models. The coastal model domains in 800 m horizontal resolution are outlined by the black rectangles, while the 160 m model domains are outlined in red. The thin black curves are 200, 500, and 1,000 m isobaths.

• A(0) = 0.2 cm2, Cultivation Trials • N(0) = 0.02, and Five cultivation trials were conducted at different locations in • C(0) = 0.4, Central Norway in 2011–2016. Seedlings with a size of 1–5mm length were produced from spores seeded either directly on as in Broch et al. (2013). For all the model domains and 6 mm carrier ropes or on 1.2 mm twines that were twisted on cultivation cycles, deployments in late summer (September) and 16 mm carrier ropes at sea deployment, following the protocol winter (February) were assumed. by Forbord et al. (2018). The growth was assessed regularly by For the county of Trøndelag, Central Norway, model measurement of the length and width of the fronds (n = 60– setups of 160 m horizontal resolution were used to provide 200). The average values of frond areas, calculated as A = 0.75 results in higher resolution for the cultivation season LW (Broch et al., 2013; Foldal, 2018) from the experiments, were February-June. The 160 m models were nested from the compared with the simulation results from the 160 m resolution 800 model for Central Norway (Figure 3). The setup and models for 2016 at the five locations. Simulated deployment dates procedure was identical to that for the 800 m models except were taken to the closest first of the month. The spatial variability that atmospheric input from a high resolution setup of in the simulation results was evaluated by considering also the WRF (https://www.mmm.ucar.edu/weather-research-and- minimum and maximum simulated frond areas from the 3 × forecasting-model) was used instead of the ECMWF-data 3 nearest model grid cells to the one matching the cultivation in order to include finer scale topographic effects close to locations. land.

Comparing Simulation Results With Field Surveys of Natural Populations Cultivation and Survey Data on S. latissima Wild sugar kelp coverage data were collected from the coast Two complementary methods for evaluating the model of Southern Norway, covering the coastal regions of the performance were applied. The first (section Cultivation trials southern Norwegian Sea, the North Sea and the Skagerrak below) compared the simulated frond area of sugar kelp with (Figure 4). Coverage was recorded using an underwater video measured averages from five cultivation trials in Central Norway. camera and georeferencing of stations at which coverage was The second (section Field surveys of natural populations below) defined semi-quantitatively at a five-step scale: 0 (absent), related the simulation results on kelp cultivation potential 1 (single individuals), 2 (scarce, i.e., a few individuals), 3 to the field observed coverage of naturally growing sugar (common/moderately dense, i.e., many plants, but not a kelp. completely dense canopy cover), or 4 (dominating, i.e., a

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9 cells were land cells, the entire survey data point was excluded from the comparison. Evaluation of the Cultivation Potential The simulation results were used to evaluate the cultivation potential for S. latissima in two diﬀerent ways, by calculating a spatial “index” for the cultivation potential and by upscaling the biomass yield from individual level to large scale yields per unit area.

Spatial Index The spatial index provides a means for comparison of the simulated kelp cultivation potentials at different locations, represented here by the different model grid cells, without considering the absolute yields. This is a comparison of the basic cultivation potential disregarding variables such as the choice of cultivation technology, farm sizes, or seeding density. It is calculated from the simulated DW of individual plants deployed in September and February (see section Model simulations above) over the seasons from 2012 to 2015. The DW yield of single individuals was integrated from deployment until the first part of June (June 11) and from 1 to 8 m depth, and the results from the September and February deployments were added. The production potential in each grid cell is normalized by the maximum over the entire region covered by the four 800 m model domains. Formally, denote by

Dn,d(x, y, z)

the simulated dry biomass of a kelp individual in (spatial) position (x, y, z) from deployment d (September or February) in FIGURE 4 | Locations for ﬁeld survey data of wild sugar kelp population the year n. Let further (S. latissima) coverage in southern Norway.

2015 8 I˜ x, y = Dn,sep x, y, z + D (x, y, z) dz . n,feb completely dense canopy cover). The data on kelp coverage was n=2013 1 collected through the National program for mapping biodiversity (Bekkby et al., 2013), the sugar kelp monitoring program (Moy The spatial index is then calculated as et al., 2009), the SaccRef sugar kelp research project (Gundersen et al., 2012), and the coastal monitoring program ( e.g., Dale et al., I˜(x, y) 2018; Fagerli et al., 2018). I x, y = . (1) max I˜(x, y) Simulation results from SINMOD using the southernmost x,y model domain in Figure 3 for 2013, 2014, 2015, and 2016 were compared with the survey data on sugar kelp coverage. The basis Biomass Estimates −1 for comparison was the frond area computed by the model from The large scale biomass yields Bn,d (t wet weight ha ) September to June. The frond area is the main structural variable at horizontal position (x,y) were upscaled from individual in the kelp model and the variable that corresponds most closely wet weight by assuming cultivation on vertical droppers and to the kelp coverage observed in field surveys. For each survey multiplying individual wet weights Wn,d = Wn,d(x, y, z) (year n data point the average simulated frond area in the 3 × 3 model and deployment d) at harvest in June by the number of plants grid cells in SINMOD, at a position and depth corresponding to per meter rope segment and the number of vertical droppers per the field survey data point, was used. The bottom layer was used hectare: whenever the model depth was less than the actual depth. The 3 × 3 arrays were used because the exact position might potentially z2 be represented by a “land point” in the model and in order to Bn,d x, y = ρNd Wn,d(x, y, z) dz (2) reduce the effect of local variability in the model. Whenever all z1

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FIGURE 5 | Simulated depth integrated (0–10 m) nitrate concentration (A) and temperature at 5 m depth (B) along the Norwegian maritime baseline (2012–2013 season). See Figure 13. The colors represent the nitrate concentration (resp. temperature) at the corresponding latitude and time along the maritime baseline.

for n = 2013, 2014, 2015 and d = Feb, Sep. Here, z1 and z2 denote north than in the south (Figure 5A). The seasonal variation the depth limits for cultivation, ρ is the number of droppers in temperature was greater in the south than in the north per hectare, and Nsep (Nfeb) is the number of individuals per (Figure 5B). The temperatures along the maritime baseline rarely meter rope segment at harvest for the September and February exceeded 15◦C, used as the upper limit for optimal growth in the deployments, respectively. Here, we have used z1 = 1 and z2 = 6, S. latissima growth model (Bolton and Lüning, 1982; Broch and ρ = 2,000, Nsep = 75, Nfeb = 150, following (Broch et al., 2013). Slagstad, 2012). The harvest of the entire biomass was assumed to take place on June 11. In order to avoid overlap with natural macroalgal Comparing Simulation Results With populations, occurring down to about 50 m depth, only the Cultivation and Survey Data model grid cells with bottom depth >50 m were considered The model reproduced realistic values for the average sizes of here. the cultivated plants (Figure 6). The model also reproduced the Average values for biomass yields, for each season and each ranking of the cultivation stations in terms of frond sizes. In some deployment, were computed for each of the six Norwegian cases there was substantial spatial variability in the simulation marine ecoregions (Direktoratsgruppen vanndirektivet, 2018; see resultsaswellasinthecultivationdata(cf.1,2,and5in Figure 6). also Figure 6) as follows: There was a clear relationship between the simulated sugar kelp frond area and the coverage of naturally occurring sugar kelp along the coast of southern Norway (Figure 7). The simulated −1 mean frond areas increased significantly from locations with no B j = Area j B x, y dA , (3) n,d n,d naturally growing sugar kelp (class 0) to locations with single j individuals (class 1). There was also an increase in simulated mean frond areas from locations with single individuals (class 1) where j ,j = 1, ..., 6, denote the ecoregions. to locations with a few individuals (scarce occurrences, class 2). Going from class 2 (scarce) to 3 (moderately dense kelp forest), RESULTS the increase in simulated average frond areas was significant, except in 2015. There was no difference in simulated mean The Environmental Variables Simulated by frond areas in locations with moderately dense kelp forest SINMOD (class 3) to completely dense canopy forest (class 4). A partial Nitrate concentrations and water temperature were extracted comparison of simulation results with the kelp coverage data from the simulation results along the maritime baseline, using a higher resolution model was made, without significantly consisting of straight line segments between the outermost points different results. of some islands or protruding points of the main land along the coast (Harsson and Preiss, 2012; Figure 13). Both variables Kelp Cultivation Potential followed a clear seasonal pattern (Figures 5A,B). The period of The Index high nutrient concentrations was shorter in the south than in The index for the cultivation potential for S. latissima (1) had the north, and the highest concentrations were higher in the a tendency to increase with distance from the shore (Figure 8).

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FIGURE 7 | Comparison of simulated frond areas from four growth seasons (2013–2016) with observed sugar kelp coverage of wild populations, from field survey data. The locations for the sampling points of the field surveys are shown in Figure 4. Kelp coverage was defined semi-quantitatively as a five-step scale: 0 (absent), 1 (single individuals), 2 (scarce, i.e., a few FIGURE 6 | Simulated (ordinate axis) against observed (abscissa) S. latissima individuals), 3 (common/moderately dense, i.e., many plants, but not a frond areas for the 5 cultivation trials in Central Norway from 2011 to 2016. completely dense canopy cover), or 4 (dominating, i.e., a completely dense The observed values are means of n = 60–200 individuals (section Cultivation canopy cover). The five-step scale is displayed along the abscissa. Mean trials). The ordinate values are means of the simulated frond area in 3 × 3 grid simulated frond areas (section Cultivation trials) for the locations in each of the cells around the cultivation location. The vertical lines represent the range of five categories, for each of the four seasons, are represented by circles. The the minimum to the maximum simulated frond area within these 3 × 3 grid vertical line segments are 95% confidence intervals for the means of the cells. The numbers (1 to 5) in the plot correspond to the different cultivation simulated frond areas for each of the five categories. The number of locations shown in Figure 9. The colors indicate cultivation depth (2, 5, or observations for each kelp population density are included. The field data 8 m). At stations 3, 4, and 5 more than one cultivation depth was used. come from different Norwegian monitoring programs; see text for details and references.

In general, the best locations for cultivation were outside the six Norwegian ecoregions outlined by curves of different colors, the bottom depth and with some of the greatest potential in the outer limits of which lie 1 nautical mile beyond the maritime region. baseline. The index was highest beyond the geographic shelf break (bottom depth > 500m; e.g., Figure 8, 1) in Central Norway and beyond the Norwegian trench (Figure 8, 2) outside Biomass Estimates western Norway. There were large regions on the relatively wide The average S. latissima cultivation potential, expressed in terms shelf outside Central Norway with a high production potential of biomass yield per unit area (3), was similar between the six (Figure 8, 3). Most coastal and fjord locations got lower scores. ecoregions for the September deployments (Figure 10, left bars). The index was similar in the Oslo fjord (Figure 8, 4) and the The southern Norwegian Sea (the purple bars in Figure 10) Porsanger fjord (Figure 8, 5) despite a latitudinal distance of 10 tended toward a slightly higher biomass yield for the September degrees. There were some minor discontinuities in the data along deployment than the other regions (average over 4 years 20% the interfaces between the model domains, but the overall picture higher than in Skagerrak), while the Barents Sea (light blue bars) was consistent (Figure 8). had yields in the low end (average over 4 years 25% lower than in The index calculated for a part of the coastal region of Skagerrak). Central Norway from the 160 m simulations revealed more For the February deployments (the rightmost bars for each details (Figure 9, which uses a different colormap than Figure 8, region, Figure 10), the yields were generally lower than for normalized only with respect to the maximum within the the September deployments, except for the northern Norwegian region presented there). The fjords (e.g., the Trondheim fjord, Sea (green bars). In particular in the three southernmost Figure 9, A) had a relatively low potential for cultivation of regions (Skagerrak, North Sea S and N) the yields from the S. latissima, though there was some local variation, cf. the February deployments were significantly lower than from the western and central parts of the Trondheim fjord. Among September deployments, also in a statistical sense. Averaging sheltered locations outside the fjords, station 3 seems to have over the Norwegian coast as a whole, there was little difference good potential as well as station 2. Frohavet (B) is a large between deploying in autumn and in late winter. Deployment region with a relatively high potential, albeit with a bottom in September yielded similar averaged and maximum values depth of 100–400 m and possibly large waves. The area just throughout all the regions, though with a peak in the Norwegian south of Froan (C) is more sheltered than B, with shallower Sea regions.

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FIGURE 8 | A spatial index for the cultivation potential of S. latissima in Norway, calculated according to (1) based on simulations using the model domains of 800 m horizontal resolution. The colored regions indicate the 6 coastal marine ecoregions in Norway: Skagerrak (dark blue), the North Sea (S) (orange), the North Sea (N) (yellow), the Norwegian Sea (S) (purple), the Norwegian Sea (N) (green), and the Barents Sea (light blue). The gray curves indicate the 200, 300, and 500 m isobaths. The extent of the map into the ocean is limited by the extent of the model domains (Figure 3). The details show the regions of Skagerrak (to the south) and the Barents Sea (to the north). The numbers refer to speciﬁc locations discussed in the text and used in Figure 11.

Within each of the regions there was a significant interannual kelp growth in the south (Skagerrak) continued through autumn, variation in the cultivation potential for S. latissima, expressed by winter and spring (Figure 12, continuous blue curve), while the vertical extent of the bars (Figure 10). The spatial variation in in the north (Norwegian Sea, S) there was no net growth the production potential was also significant, as indicated by the between early December and late January (continuous red positions of the circles relative to the vertical bars in Figure 10, in curve). In the far north (Barents Sea) the average frond size particular in Skagerrak and the northern part of the Norwegian even declined slightly from late November until early February Sea. For the country as a whole, the interannual variation was not (continuous yellow curve). For the February deployment the great. growth in the Barents Sea caught up with that in Skagerrak Outside the maritime baseline (locations 1 to 3 in Figure 8) quite early. The simulated frond sizes of the plants from the the cultivation potential was typically comparable to the February “deployment” in the Barents Sea surpassed those of maximum for the inside of the line, with the highest potential the Skagerrak plants by about an order of magnitude by early yields off the shelf break (Figure 11). June. The variation in frond sizes between different locations in the Skagerrak region was greater than in the other two regions, Time and Latitude throughout the entire growth period and for both deployment There were differences in the average (geometric mean) seasonal times studied, as seen from the (geometric) standard deviations growth pattern between the southern and northern ecoregions (Figure 12, shaded regions). (Figure 12). While growth initiated later in the Barents Sea While the period of the maximal biomass specific growth rate than in the Skagerrak, the harvestable size of the plants in the (excluding the very first period after deployment) was February- beginning of June was similar. When deployed in September, March, the absolute daily biomass growth was highest from the

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FIGURE 9 | Spatial index for the cultivation potential of S. latissima in Central Norway, in the Norwegian Sea (S) region, calculated according to (1) based on results from model simulations of 160 m horizontal resolution (see section Model simulations). The coloring scheme used here is different from the one in Figure 8, and direct comparisons between Figure 8 and this ﬁgure do not make sense. The city of Trondheim (63.4◦N ; 10.4◦E) is indicated, as well as the stations 1 to 5 of the cultivation trials reported in Figure 6. The black line segment indicates the scale of the region covered. The gray curves are 100, 200, and 300 m isobaths. A, B, and C indicated the Trondheim fjord, Frohavet, and the southern end of the Froan archipelago, respectively.

FIGURE 10 | Spatial averages for the kelp production potential over the 6 ecoregions in Figure 8 calculated according to (3), taking into account only the grid cells with bottom depth > 50 m. The colors correspond to the colors outlining the regions in Figure 8. The bars span from minimum to maximum spatial means over the cultivation seasons 2012–2015. The ﬁlled-in dots are the average maxima, over all seasons, for each region. The left bars and dots for each region indicate deployment in September, whereas the right ones represent deployment in February the following year). Harvest in the beginning of June is assumed.

end of April into July (Figure 13). There was a clear trend in the absolute daily growth rates are calculated as ln(Bn+1) - ln(Bn) and timing of the maximal daily (dry) biomass growth along the outer Bn+1- Bn , respectively. boundary of the ecoregions for the September deployments. There was up to 2 months between the timing of the maximal daily growth rate from the southernmost and northernmost DISCUSSION points along the coast at 58 ◦ and 71 ◦N, respectively, both for absolute and speciﬁc daily growth rates. Recall that if Bn and Bn+1 This paper highlights important aspects of the cultivation denote the biomass on 2 consecutive days, then the speciﬁc and potential for the kelp species S. latissima in Norway such as

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FIGURE 11 | The cultivation potential of S. latissima, calculated according to (2), at the locations labeled 1 to 5 in Figure 8. The vertical range of the bars indicate the minimum to the maximum production over the seasons 2012–2015.

FIGURE 12 | Time series for average (spatial geometric mean) simulated frond areas within the Skagerrak (blue lines), Norwegian Sea S (red lines), and Barents Sea (yellow lines) ecoregions (see Figure 8) at 1.5 m depth. The continuous lines represent deployments in September, while the dashed lines represent deployments in February. The shaded regions indicate the geometric standard deviation factors within each region. Note the logarithmic scale of the ordinate axis. location (fjord, nearshore, offshore) and latitude. How the key sizes of plants and is able to reproduce spatial differences environmental variables for growth vary and interact in time and between locations (e.g., 1 and 2 in Figure 6). Secondly, because space and how these affect the timing of deployment and the the simulated production potential is related to suitability of cultivation cycle are also considered. The evaluation of the results locations for growth of natural kelp populations. This indicates against cultivation data and field survey data on the density of a potential for the model to identify actual locations suitable natural populations of S. latissima support the results, but there for kelp cultivation. The simulation results further indicate is yet no empirical data on the offshore kelp cultivation potential. a potential for kelp cultivation in many regions in which naturally growing S. latissima is absent (Figure 7). This is Model Evaluation and Uncertainty because the model simulates the suitability of areas for kelp The evaluation exercise performed here (section The production in the water masses as a function of the prevailing environmental variables simulated by SINMOD) is important physical and chemical conditions, regardless of the suitability for at least two reasons. Firstly, because it shows that the of the seabed for growth of natural kelps and their possibilities dynamical model (SINMOD) provides realistic values for of completing a full life cycle. The model was not tuned to

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FIGURE 13 | The effect of latitude on the timing of biomass development of S. latissima deployed in September. The simulated DW of individuals was sampled along the baseline (blue line along the Norwegian coastline) from the 2013–2015 simulations. The yellow dots indicate the average time (abscissa axis) of the highest daily speciﬁc growth rates plotted against latitude (ordinate axis on the right) over the three seasons. The red dots indicate the average time of the highest absolute daily growth rate against latitude over the three seasons. The small gray dots indicate the data used to compute the averages.

any of the data used in the present paper. Previous partial Index, Biomass Estimates, and Production validations have been provided in Broch et al. (2013) and Potential Fossberg et al. (2018). The main result from the spatial index estimates is that Some North Atlantic kelp species have been found to have a the potential seemed to be higher offshore than nearshore. clear response to (changes in) day length (Lüning, 1993; Rinde There are several explanations for this. Firstly, in fjords and and Sjøtun, 2005; Krause-Jensen et al., 2016), but the exact in the Norwegian Coastal Current (NCC) the surface waters “functional response” of S. latissima to these environmental have lower salinity, which may have an adversary effect on signals is not known. It is difficult to formulate realistic growth and production in S. latissima (Mortensen, 2017). mathematical growth models without taking the photoperiod Secondly, the nearshore stratification generally leads to an into account in some way or another, in particular at relatively earlier phytoplankton spring bloom, with ensuing earlier nutrient high latitudes (Broch and Slagstad, 2012). depletion of the surface waters (Figure 5A) and a shorter period The key environmental variables limiting macroalgal of nutrient uptake than further offshore. Thirdly, the coastal and growth have been considered here. These are nutrients, light, nearshore waters have a higher light attenuation than oceanic temperature, and ocean currents (Wiencke and Bischof, waters, which means that cultivation must take place closer to 2012), but there are also a number of variables that have the surface. As CDOM and silts were not explicitly included in not been taken into account. It has been assumed here that the calculations of the light attenuation, the light attenuation may nitrogen is the main (macro) nutrient limiting growth (Hurd have been under estimated in nearshore regions, to the effect et al., 2014). Phosphorous may also be of importance in that differences in the index between near- and offshore regions certain cases. Micronutrients and trace elements have not might have been greater. Finally, the temperatures in the coastal been considered, nor has the concentration of CO2. Epiphyte waters are generally lower in winter (potentially limiting growth) infestation is of great importance for macroalgae aquaculture, and higher in summer (potentially harmful) than in oceanic as well as exposure and wave height (van der Molen et al., waters. 2018). Despite the production potential being higher offshore than In the present simulations the same set of numerical values for nearshore, there were regions of locally higher production the kelp growth parameters were used (Broch and Slagstad, 2012; potential along the coast. This was also the case for relatively Broch et al., 2013; Fossberg et al., 2018) for the entire Norwegian sheltered regions as seen from the 160 m model simulations. coast. Further data on responses of ecotypes will provide a better These are regions that usually have high vertical mixing, basis for modeling and thus the understanding the true role typically tidal mixing and upwelling events, providing of changes in environmental conditions for the growth of S. nutrients from deeper waters also after the spring bloom latissima. period.

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The index introduced here has similarities to the suitability which follows a latitudinal gradient (Vikebø et al., 2012). index for S. latissima cultivation developed by van der Molen Since the values used in Figure 5A were extracted along et al. (2018). The difference is that our index uses results the maritime baseline, some sample points were further from a kelp growth model rather than just the environmental offshore than others, explaining some of the variability in the data provided by the ecosystem model directly, and that results. it is based on higher spatial model resolution (800/160m We have assumed here a fixed harvest time in the beginning vs. 5.5 km). This means that temporal interactions between of June for consistency. This is later than what is common in internal (nitrogen and carbon) reserves were taken into commercial kelp farms mainly due to the onset of epiphyte account and integrated over time and over the entire model infestations in late spring/early summer (Førde et al., 2016). domain. Harvest time in the north may have been assumed to take place The biomass estimates are based on upscaling (2) and must earlier than necessary and prolonging the growth season would therefore be considered over estimates. However, this is not increase the production potential in the north. The harvest date necessarily the case for the upper limits for the production may impact on the composition of the biomass, with implications potential. The relative differences in the production potentials for its relevance for various end products (Sharma et al., 2018). remain valid despite the uncertainties in upscaling. Biomass The greatest potential for S. latissima production seems to lie production estimates from other sources are presented in in relatively exposed regions and in oceanic waters outside the Table 1. The figures have in most cases been transformed coastal zones. We have not considered the existence of suitable by the present authors to the common unit “t (wet weight) technology for macroalgae cultivation in such environments, ha−1.” Our biomass estimates have been based on averages over but there have been successful cultivation trials and some very large regions (∼1,000 to ∼10,000 km2), with substantial technological development in this direction (Buck and Buchholz, spatial variation within each of these. The results in Table 1 2005; Buck et al., 2017; Bak et al., 2018). However, deploying indicate that cultivation yields may vary by an order of and harvesting kelp cultures far offshore will increase transport magnitude even within a fairly small region (Bruhn et al., 2016). and energy costs, which might outweigh the benefits of increased This emphasizes that local considerations are important when biomass (Burg et al., 2017). siting a farm (see section Future prospects and management Many of the considerations presented here probably remain below). valid for other kelp species, like Laminaria digitata and Alaria The results indicate that the production potential for the esculenta. Different tolerance levels for, e.g., low salinity points February deployments increased moving northward among the toward a potential for using, e.g., L. digitata in fjord environments six marine ecoregions. This does not mean that (pelagic) primary where it may have an advantage over S. latissima (Kerrison et al., production in general increases with latitude. Rather, this finding 2015; Mortensen, 2017). reflects the earlier onset of the phytoplankton spring bloom in the south. This leads to an earlier depletion of the nutrients in Environmental Effects and Interactions the surface layer and thus a shorter time-period of high nutrient There are most likely both positive and negative impacts of concentrations available for cultivated macroalgae in the south macroalgae cultivation on coastal environments. Examples of than in the north, in particular with harvest in early June. The potentially positive effects are that cultivation of macroalgae results on the production potential simulated for September may mitigate effects of ocean acidification (Duarte et al., 2017; seem to be more consistent between the ecoregions along the Krause-Jensen et al., 2018) and eutrophication (Xiao et al., coast. 2017). Examples of negative effects are reduced oxygen levels Along the same line, there seems to be a clear trend toward due to loss and degradation of organic matter or nutrient a later development moving northwards (Figures 12, 13). This is depletion in oligotrophic areas. At the moment European due to the combined effects of temperature, total light availability, kelp farms are relatively small, with a total production of photoperiod and nutrient availability. S. latissima production <1,000 tons a year (FAO, 2016), and at such a level large in the northern regions in some places seems to “catch up” scale negative effects seem unlikely. Some studies on the with that in the southern regions. Thus, there is a potential bene—fits and effects of macroalgal cultivation both from for the macroalgae aquaculture industry to provide biomass at China (Zhang et al., 2009; Xiao et al., 2017) and Europe stable volumes for an extended time period. These results further (Walls et al., 2017; Hasselström et al., 2018) have been underscore the importance of distinguishing between specific made. and absolute growth. The latter is probably most important for Here, the focus has been on mapping the potential for biomass farming purposes. production. Evaluation of different production regions may also The seasonal and geographic patterns in growth be performed with a view to, e.g., minimize environmental effects development mirrored the environmental variables like (nutrient depletion, organic enrichment by dislodges plants and nutrient concentrations and temperature to some extent. eroded tissue) or interactions between kelp farms and other The numerical values of the nutrient concentrations and types of aquaculture (spread of diseases and parasites between the qualitative features of their seasonal development farms etc.). We have not considered Integrated Multi-Trophic match those of observations (Sætre, 2007). The seasonal Aquaculture (IMTA) in this study, though this is a practice pattern of surface nutrient concentrations is to a great that may increase biomass yields locally (Broch et al., 2013; extent governed by the phytoplankton bloom dynamics, Handå et al., 2013; Fossberg et al., 2018; Jansen et al., 2018) and

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TABLE 1 | Some previously published estimates for the kelp cultivation potential in different regions.

− Species Biomassyield(tha 1) Location Remarks References

Laminaria hyperborea 90–270 Norway Recalculated from an annual biomass Abdullah and production of 9–27 kg m−2 Fredriksen, 2004; Rinde et al., 2006 S. latissima 220 Scotland Upscaled from small scale ﬁeld trials in Sanderson et al., 2012 Integrated Multi-trophic Aquaculture (IMTA) S. latissima 30–40 Galicia, Spain Peteiro and Freire, 2013 S. latissima 95 Eastern Canada Recalculated by the present authors from a Reid et al., 2013 yield of 19.95 t per 0.21 ha Alaria esculenta 63 Eastern Canada Recalculated by the present authors from a Reid et al., 2013 yield of 13.3 t per 0.21 ha S. latissima 200 Norway Upscaled by present authors Matsson et al., 2015 Saccharina japonica 162 China Average Chinese production calculated by the Zhang et al., 2015 present authors (total production/allocated area) assuming dry matter content of 15% S. latissima 0.2–1.4 Limfjorden, Denmark Upscaled by present authors from biomass Bruhn et al., 2016 yields m−1 vertical rope culture, 2.5 deep, assuming 2,000 ropes ha−1, S. latissima 22. 5- 27.6 Sweden Upscaled from small scale ﬁeld trials by present Pechsiri et al., 2016 authors S. japonica 32 Sanggou Bay, China Average for Sanggou Bay, China, calculated by Zhang et al., 2016 present authors from dry biomass, assuming dry matter content of 15 % Macroalgae sensu lato 106 China Calculated from average for total Chinese Xiao et al., 2017 production (total production/allocated area) by the present authors assuming dry matter content of 15 % S. latissima 383 Central Norway Upscaled by the present authors from reports Sharma et al., 2018 on a production of 38.3 kg m−2 from February to June S. latissima 75 Norway Model based estimate for average within entire Present study Norwegian baseline. S. latissima 230 Norway Model based estimate for maximal potential Present study within entire Norwegian baseline with deployment in September.

The ﬁgures all have the unit “wet weight ha−1 per season.” In most cases the ﬁgures have been recalculated or up-scaled from the original source to this unit by the present authors. Details on the original data are given.

may be particularly attractive in regions where space is limiting Directorate of Fisheries, 2018), with S. latissima at present development of aquaculture. being the commercially most important species. The total kelp In reality, a large kelp farm may experience a reduction production for 2017 amounted to 145 tons with a sales value in the transport of nutrients within the farm due to nutrient of approximately 74,000 Euro (Ibid.). It has been suggested that uptake by the kelp, or the plants may experience light by 2050 the Norwegian aquaculture industry could produce 20 shading due to the se—eding density (Broch et al., 2013). million tons of kelp with a turnover of 4 billion Euro annually It has not been possible to address these aspects here, (Olafsen et al., 2012). According to the average production as a two-way feedback between the kelp and ecosystem potential for the entire Norwegian coast in Figure 10 this would module in the present simulations would result in substantial require an area in the range of 2,700–3,000 km2. By comparison nutrient uptake everywhere, and hence would prevent an the sea area inside the Norwegian maritime baseline covers assessment of the baseline conditions for the cultivation 89,091 km2, excluding Svalbard and Jan Mayen. Such ﬁgures are potential. deceptively large as there are a multitude of interests competing for space in the coastal zone, illustrated by van der Molen Future Prospects and Management et al. (2018). Thus, careful planning is required for successful By January 2017 there were a total of 309 permits for macroalgae siting of macroalgae cultivation facilities and to optimize value cultivation in Norway, of which roughly half were awarded for chains from deployment of seedlings, through harvest and kelp cultivation (S. latissima, L. digitata, A. esculenta)(Norwegian processing, to the end market of the products based on the

Frontiers in Marine Science | www.frontiersin.org 13 January 2019 | Volume 5 | Article 529 Broch et al. The Kelp Cultivation Potential in Norway biomass. GIS-type analyses will be necessary, and the present surveys. All authors contributed to the analysis of the results and results provide a basis for this. Future constraints may include the writing of the paper. minimizing environmental impacts and sources of pollution. Future possibilities lie in moving from the coastal region into the FUNDING ocean. The research reported in this paper was mainly supported by AUTHOR CONTRIBUTIONS the Research Council of Norway grants 267536 (KELPPRO) and 254883 (MACROSEA) and by the county authorities of OB, MA, TB, HG, SF, AH, JS, and KH all conceived and Møre-and-Romsdal and Trøndelag. HPC resources were made planned the article. OB and MA performed the coupled model available through NOTUR grant no. NN2967k. Two of the simulations. SF, JS, and AH executed the kelp cultivation trials cultivation trials were executed in the Research Council of and sampling. TB and HG contributed to the natural kelp ﬁeld Norway project 244244 (PROMAC).

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Frontiers in Marine Science | www.frontiersin.org 15 January 2019 | Volume 5 | Article 529

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